Today the term neural network or, more properly, artificial neural network (ANN), has come to mean any computing architecture that consists of massively parallel interconnection of simple "neural" processors. Neural networks are used in pattern classification by defining non-linear regions in the feature space. While neural networks are relatively new in regard to operation, they have been investigated by researchers for many years. For example, see The Proceedings Of The IEEE, September 1990, Special Issue on Neural Networks, Vol. 8, No. 9, pp. 1409-1544. Thus, neural networks of many different types of configurations and uses are well known.
The basic building block used in many neural networks is the adaptive linear element or Adaline. This is an adaptive threshold logic element which comprises an adaptive linear combiner cascaded with a hard limiting quantizer which is used to produce a binary output. In single element neural elements an adaptive algorithm is often used to adjust the weights of the Adaline so that it responds correctly to as many patterns as possible in a training set that has binary desired responses. Once the weights are adjusted, the responses of the trained element can be tested by applying various input patterns. If the Adaline responds correctly with high probability to input patterns that were not included in the training set, it is said that generalization has taken place. Thus, learning and generalization are among the most useful attributes of Adalines in neural networks. A single Adaline is capable of realizing only a small subset of certain logic functions known as the linearly separable logic functions or threshold logic functions. These are the set of logic functions that can be obtained with all possible weight variations. Thus, such classifiers are sometimes referred to as linear classifiers. The linear classifier is limited in its capacity and is limited to only linearly separable forms of pattern discrimination.
More sophisticated classifiers with higher capacities are non-linear. There are two types of non-linear classifiers which are known in the art. The first is a fixed pre- processing network connected to a single adaptive element and the other is the multi-element feed forward neural network. These networks, as well as operations and configurations are known in the prior art and are described, for example, in the above- noted publication in an article entitled "Thirty Years of Adaptive Neural Networks: Perception, Madaline, and Backpropagation" by Bernard Widrow et al. in Proceedings of the IEEE, Vol. 78, No. 9, September, 1990, pp. 1415-1441. The article, besides describing the history of neural networks as of the publication date, also has an extensive bibliography in regard to the same.
It is thus apparent that classification, which effects the partitioning of items into separate groups of similar members, is performed using several different artificial neural network (ANN) classifiers. Thus, there exist many prior art publications which show such classifiers. See, for example, the following references:
G. A. Carpenter and S. Grossberg, "The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network" Computer, pp 77-88, March, 1988. PA1 A. Mekkaoui and P. Jespers, "An Optimal Self-Organizing Pattern Classifier", Proceedings of the International Joint Conference on Neural Networks, Vol. I, pp. 447-450, Washington, D.C., Jan. 15-19, 1990. PA1 R. Wilson and M. Spann, "A New Approach To Clustering" Pattern Recognition, Vol 23, No. 12, pp. 1413-1425, 1990. PA1 R. Hecht-Nielsen, "Counter-Propagation Networks" IEEE First International Conference on Neural Networks, Volume II, pp. 19-32. PA1 K. Rose, E. Gurewitz, and G. Fox, "A Deterministic Annealing Approach to Clustering" Pattern Recognition Letters,pp. 589-594, September, 1990. PA1 T. Kohonen, "Self-Organization and Associative Memory" Springer-Verlag, 3rd ed Berlin, Heidelberg, Germany, 1989. PA1 P. Breitoph and R. Walker, "Spectral Clustering With Neural Networks for Application to Multispectral Remote Sensing of Minerals", Proceedings of the First Annual INNS Meeting,Boston, 1988 PA1 R. Namani, P. H. Patrick, W. G. Hanson and H. Anderson, "Fish Detection and Classification Using a Neural-Network--Based Active Sonar System--Preliminary Results", Proceedings of the International Joint Conference on Neural Networks, Vol. II, pp. 527-530, Washington, D.C., Jan. 15-19, 1990. PA1 R. Gorman and T. Sejnowski, "Learned Classification of Sonar Targets Using a Massively Parallel Network", IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-36, pp. 1135-1140, 1988.
These all pertain to different types of artificial neural network classifiers for different purposes. Applications of artificial neural network base classification include the spectral clustering of minerals, as shown for example in the following publication:
Other prior art use ANNS for the classification of fish and sonar targets, as disclosed in the following two references:
See also the Proceedings of the IEEE, October, 1990, Vol. 78, No. 10, which is a special issue on neural networks as is the above issue and which also has articles showing radar signal categorization using a neural network by James A. Anderson, et al., on p. 1646. Thus, as one can ascertain, there are a number of different neural network configurations which are operated as classifiers and such classifiers are well known.
The classifier essentially receives a feature set at a plurality of inputs where each of the inputs is directed to individual processing elements which comprise the neural network. The feature sets are then partitioned into separate classes which appear at the outputs. As will be explained, ANN classifiers may also determine that a separate course of action be undertaken depending upon which output class is selected by the ANN classifier. In this manner, such a classifier is intimately associated with a processor which, as will be explained, enables the system, including the ANN, to provide meaningful data and to further process the data or operate on the data, according to the output class.
In order to use the process, which can be accessed randomly, depending upon the classifier's operation, a great deal of time is usually required. Many processors are absolutely dedicated to the sole task of processing the outputs from the neural network and cannot be used for other routines.
It is therefore an object of the present invention to provide apparatus and a method which enables the output class firing of an ANN classifier to rapidly trigger the execution of the appropriate computer routine by using the rising edge, zero to one transition, of the ANN output as the interrupt input to a digital computer.